Artificial selection reveals complex genetic architecture of shoot branching and its response to nitrate supply in Arabidopsis.

Quantitative traits may be controlled by many loci, many alleles at each locus, and subject to genotype-by-environment interactions, making them difficult to map. One example of such a complex trait is shoot branching in the model plant Arabidopsis, and its plasticity in response to nitrate. Here, we use artificial selection under contrasting nitrate supplies to dissect the genetic architecture of this complex trait, where loci identified by association mapping failed to explain heritability estimates. We found a consistent response to selection for high branching, with correlated responses in other traits such as plasticity and flowering time. Genome-wide scans for selection and simulations suggest that at least tens of loci control this trait, with a distinct genetic architecture between low and high nitrate treatments. While signals of selection could be detected in the populations selected for high branching on low nitrate, there was very little overlap in the regions selected in three independent populations. Thus the regulatory network controlling shoot branching can be tuned in different ways to give similar phenotypes.

Haplotype-Based Genome-Wide Association Analysis Using Exome Capture Assay and Digital Phenotyping Identifies Genetic Loci Underlying Salt Tolerance Mechanisms in Wheat.

Soil salinity can impose substantial stress on plant growth and cause significant yield losses. Crop varieties tolerant to salinity stress are needed to sustain yields in saline soils. This requires effective genotyping and phenotyping of germplasm pools to identify novel genes and QTL conferring salt tolerance that can be utilised in crop breeding schemes. We investigated a globally diverse collection of 580 wheat accessions for their growth response to salinity using automated digital phenotyping performed under controlled environmental conditions. The results show that digitally collected plant traits, including digital shoot growth rate and digital senescence rate, can be used as proxy traits for selecting salinity-tolerant accessions. A haplotype-based genome-wide association study was conducted using 58,502 linkage disequilibrium-based haplotype blocks derived from 883,300 genome-wide SNPs and identified 95 QTL for salinity tolerance component traits, of which 54 were novel and 41 overlapped with previously reported QTL. Gene ontology analysis identified a suite of candidate genes for salinity tolerance, some of which are already known to play a role in stress tolerance in other plant species. This study identified wheat accessions that utilise different tolerance mechanisms and which can be used in future studies to investigate the genetic and genic basis of salinity tolerance. Our results suggest salinity tolerance has not arisen from or been bred into accessions from specific regions or groups. Rather, they suggest salinity tolerance is widespread, with small-effect genetic variants contributing to different levels of tolerance in diverse, locally adapted germplasm.

High-throughput phenotyping using digital and hyperspectral imaging-derived biomarkers for genotypic nitrogen response.

The development of crop varieties with higher nitrogen use efficiency is crucial for sustainable crop production. Combining high-throughput genotyping and phenotyping will expedite the discovery of novel alleles for breeding crop varieties with higher nitrogen use efficiency. Digital and hyperspectral imaging techniques can efficiently evaluate the growth, biophysical, and biochemical performance of plant populations by quantifying canopy reflectance response. Here, these techniques were used to derive automated phenotyping of indicator biomarkers, biomass and chlorophyll levels, corresponding to different nitrogen levels. A detailed description of digital and hyperspectral imaging and the associated challenges and required considerations are provided, with application to delineate the nitrogen response in wheat. Computational approaches for spectrum calibration and rectification, plant area detection, and derivation of vegetation index analysis are presented. We developed a novel vegetation index with higher precision to estimate chlorophyll levels, underpinned by an image-processing algorithm that effectively removed background spectra. Digital shoot biomass and growth parameters were derived, enabling the efficient phenotyping of wheat plants at the vegetative stage, obviating the need for phenotyping until maturity. Overall, our results suggest value in the integration of high-throughput digital and spectral phenomics for rapid screening of large wheat populations for nitrogen response.

Natural variation in Arabidopsis shoot branching plasticity in response to nitrate supply affects fitness.

The capacity of organisms to tune their development in response to environmental cues is pervasive in nature. This phenotypic plasticity is particularly striking in plants, enabled by their modular and continuous development. A good example is the activation of lateral shoot branches in Arabidopsis, which develop from axillary meristems at the base of leaves. The activity and elongation of lateral shoots depends on the integration of many signals both external (e.g. light, nutrient supply) and internal (e.g. the phytohormones auxin, strigolactone and cytokinin). Here, we characterise natural variation in plasticity of shoot branching in response to nitrate supply using two diverse panels of Arabidopsis lines. We find extensive variation in nitrate sensitivity across these lines, suggesting a genetic basis for variation in branching plasticity. High plasticity is associated with extreme branching phenotypes such that lines with the most branches on high nitrate have the fewest under nitrate deficient conditions. Conversely, low plasticity is associated with a constitutively moderate level of branching. Furthermore, variation in plasticity is associated with alternative life histories with the low plasticity lines flowering significantly earlier than high plasticity lines. In Arabidopsis, branching is highly correlated with fruit yield, and thus low plasticity lines produce more fruit than high plasticity lines under nitrate deficient conditions, whereas highly plastic lines produce more fruit under high nitrate conditions. Low and high plasticity, associated with early and late flowering respectively, can therefore be interpreted alternative escape vs mitigate strategies to low N environments. The genetic architecture of these traits appears to be highly complex, with only a small proportion of the estimated genetic variance detected in association mapping.

High-resolution mapping of rachis nodes per rachis, a critical determinant of grain yield components in wheat.

KEY MESSAGE: Exploring large genomic data sets based on the latest reference genome assembly identifies the rice ortholog APO1 as a key candidate gene for number of rachis nodes per spike in wheat. Increasing grain yield in wheat is a key breeding objective worldwide. Several component traits contribute to grain yield with spike attributes being among the most important. In this study, we performed a genome-wide association analysis for 12 grain yield and component traits measured in field trials with contrasting agrochemical input levels in a panel of 220 hexaploid winter wheats. A highly significant, environmentally consistent QTL was detected for number of rachis nodes per rachis (NRN) on chromosome 7AL. The five most significant SNPs formed a strong linkage disequilibrium (LD) block and tagged a 2.23 Mb region. Using pairwise LD for exome SNPs located across this interval in a large worldwide hexaploid wheat collection, we reduced the genomic region for NRN to a 258 Kb interval containing four of the original SNP and six high-confidence genes. The ortholog of one (TraesCS7A01G481600) of these genes in rice was ABBERANT PANICLE ORGANIZATION1 (APO1), which is known to have significant effects on panicle attributes. The APO1 ortholog was the best candidate for NRN and was associated with a 115 bp promoter deletion and two amino acid (C47F and D384 N) changes. Using a large worldwide collection of tetraploid and hexaploid wheat, we found 12 haplotypes for the NRN QTL and evidence for positive enrichment of two haplotypes in modern germplasm. Comparison of five QTL haplotypes in Australian yield trials revealed their relative, context-dependent contribution to grain yield. Our study provides diagnostic SNPs and value propositions to support deployment of the NRN trait in wheat breeding.

Durum wheat genome highlights past domestication signatures and future improvement targets.

The domestication of wild emmer wheat led to the selection of modern durum wheat, grown mainly for pasta production. We describe the 10.45 gigabase (Gb) assembly of the genome of durum wheat cultivar Svevo. The assembly enabled genome-wide genetic diversity analyses revealing the changes imposed by thousands of years of empirical selection and breeding. Regions exhibiting strong signatures of genetic divergence associated with domestication and breeding were widespread in the genome with several major diversity losses in the pericentromeric regions. A locus on chromosome 5B carries a gene encoding a metal transporter (TdHMA3-B1) with a non-functional variant causing high accumulation of cadmium in grain. The high-cadmium allele, widespread among durum cultivars but undetected in wild emmer accessions, increased in frequency from domesticated emmer to modern durum wheat. The rapid cloning of TdHMA3-B1 rescues a wild beneficial allele and demonstrates the practical use of the Svevo genome for wheat improvement.

Genebank genomics highlights the diversity of a global barley collection.

Genebanks hold comprehensive collections of cultivars, landraces and crop wild relatives of all major food crops, but their detailed characterization has so far been limited to sparse core sets. The analysis of genome-wide genotyping-by-sequencing data for almost all barley accessions of the German ex situ genebank provides insights into the global population structure of domesticated barley and points out redundancies and coverage gaps in one of the world's major genebanks. Our large sample size and dense marker data afford great power for genome-wide association scans. We detect known and novel loci underlying morphological traits differentiating barley genepools, find evidence for convergent selection for barbless awns in barley and rice and show that a major-effect resistance locus conferring resistance to bymovirus infection has been favored by traditional farmers. This study outlines future directions for genomics-assisted genebank management and the utilization of germplasm collections for linking natural variation to human selection during crop evolution.

Exome sequence genotype imputation in globally diverse hexaploid wheat accessions.

KEY MESSAGE: Imputing genotypes from the 90K SNP chip to exome sequence in wheat was moderately accurate. We investigated the factors that affect imputation and propose several strategies to improve accuracy. Imputing genetic marker genotypes from low to high density has been proposed as a cost-effective strategy to increase the power of downstream analyses (e.g. genome-wide association studies and genomic prediction) for a given budget. However, imputation is often imperfect and its accuracy depends on several factors. Here, we investigate the effects of reference population selection algorithms, marker density and imputation algorithms (Beagle4 and FImpute) on the accuracy of imputation from low SNP density (9K array) to the Infinium 90K single-nucleotide polymorphism (SNP) array for a collection of 837 hexaploid wheat Watkins landrace accessions. Based on these results, we then used the best performing reference selection and imputation algorithms to investigate imputation from 90K to exome sequence for a collection of 246 globally diverse wheat accessions. Accession-to-nearest-entry and genomic relationship-based methods were the best performing selection algorithms, and FImpute resulted in higher accuracy and was more efficient than Beagle4. The accuracy of imputing exome capture SNPs was comparable to imputing from 9 to 90K at approximately 0.71. This relatively low imputation accuracy is in part due to inconsistency between 90K and exome sequence formats. We also found the accuracy of imputation could be substantially improved to 0.82 when choosing an equivalent number of exome SNP, instead of 90K SNPs on the existing array, as the lower density set. We present a number of recommendations to increase the accuracy of exome imputation.

Detection and validation of genomic regions associated with resistance to rust diseases in a worldwide hexaploid wheat landrace collection using BayesR and mixed linear model approaches.

KEY MESSAGE: BayesR and MLM association mapping approaches in common wheat landraces were used to identify genomic regions conferring resistance to Yr, Lr, and Sr diseases. Deployment of rust resistant cultivars is the most economically effective and environmentally friendly strategy to control rust diseases in wheat. However, the highly evolving nature of wheat rust pathogens demands continued identification, characterization, and transfer of new resistance alleles into new varieties to achieve durable rust control. In this study, we undertook genome-wide association studies (GWAS) using a mixed linear model (MLM) and the Bayesian multilocus method (BayesR) to identify QTL contributing to leaf rust (Lr), stem rust (Sr), and stripe rust (Yr) resistance. Our study included 676 pre-Green Revolution common wheat landrace accessions collected in the 1920-1930s by A.E. Watkins. We show that both methods produce similar results, although BayesR had reduced background signals, enabling clearer definition of QTL positions. For the three rust diseases, we found 5 (Lr), 14 (Yr), and 11 (Sr) SNPs significant in both methods above stringent false-discovery rate thresholds. Validation of marker-trait associations with known rust QTL from the literature and additional genotypic and phenotypic characterisation of biparental populations showed that the landraces harbour both previously mapped and potentially new genes for resistance to rust diseases. Our results demonstrate that pre-Green Revolution landraces provide a rich source of genes to increase genetic diversity for rust resistance to facilitate the development of wheat varieties with more durable rust resistance.

Genetic diversity and population structure in a legacy collection of spring barley landraces adapted to a wide range of climates.

Global environmental change and increasing human population emphasize the urgent need for higher yielding and better adapted crop plants. One strategy to achieve this aim is to exploit the wealth of so called landraces of crop species, representing diverse traditional domesticated populations of locally adapted genotypes. In this study, we investigated a comprehensive set of 1485 spring barley landraces (Lrc1485) adapted to a wide range of climates, which were selected from one of the largest genebanks worldwide. The landraces originated from 5° to 62.5° N and 16° to 71° E. The whole collection was genotyped using 42 SSR markers to assess the genetic diversity and population structure. With an average allelic richness of 5.74 and 372 alleles, Lrc1485 harbours considerably more genetic diversity than the most polymorphic current GWAS panel for barley. Ten major clusters defined most of the population structure based on geographical origin, row type of the ear and caryopsis type - and were assigned to specific climate zones. The legacy core reference set Lrc648 established in this study will provide a long-lasting resource and a very valuable tool for the scientific community. Lrc648 is best suited for multi-environmental field testing to identify candidate genes underlying quantitative traits but also for allele mining approaches.

Genetic dissection of photoperiod response based on GWAS of pre-anthesis phase duration in spring barley.

Heading time is a complex trait, and natural variation in photoperiod responses is a major factor controlling time to heading, adaptation and grain yield. In barley, previous heading time studies have been mainly conducted under field conditions to measure total days to heading. We followed a novel approach and studied the natural variation of time to heading in a world-wide spring barley collection (218 accessions), comprising of 95 photoperiod-sensitive (Ppd-H1) and 123 accessions with reduced photoperiod sensitivity (ppd-H1) to long-day (LD) through dissecting pre-anthesis development into four major stages and sub-phases. The study was conducted under greenhouse (GH) conditions (LD; 16/8 h; ∼20/∼16°C day/night). Genotyping was performed using a genome-wide high density 9K single nucleotide polymorphisms (SNPs) chip which assayed 7842 SNPs. We used the barley physical map to identify candidate genes underlying genome-wide association scans (GWAS). GWAS for pre-anthesis stages/sub-phases in each photoperiod group provided great power for partitioning genetic effects on floral initiation and heading time. In addition to major genes known to regulate heading time under field conditions, several novel QTL with medium to high effects, including new QTL having major effects on developmental stages/sub-phases were found to be associated in this study. For example, highly associated SNPs tagged the physical regions around HvCO1 (barley CONSTANS1) and BFL (BARLEY FLORICAULA/LEAFY) genes. Based upon our GWAS analysis, we propose a new genetic network model for each photoperiod group, which includes several newly identified genes, such as several HvCO-like genes, belonging to different heading time pathways in barley.

Genome-wide association studies for agronomical traits in a world wide spring barley collection.

BACKGROUND: Genome-wide association studies (GWAS) based on linkage disequilibrium (LD) provide a promising tool for the detection and fine mapping of quantitative trait loci (QTL) underlying complex agronomic traits. In this study we explored the genetic basis of variation for the traits heading date, plant height, thousand grain weight, starch content and crude protein content in a diverse collection of 224 spring barleys of worldwide origin. The whole panel was genotyped with a customized oligonucleotide pool assay containing 1536 SNPs using Illumina's GoldenGate technology resulting in 957 successful SNPs covering all chromosomes. The morphological trait "row type" (two-rowed spike vs. six-rowed spike) was used to confirm the high level of selectivity and sensitivity of the approach. This study describes the detection of QTL for the above mentioned agronomic traits by GWAS. RESULTS: Population structure in the panel was investigated by various methods and six subgroups that are mainly based on their spike morphology and region of origin. We explored the patterns of linkage disequilibrium (LD) among the whole panel for all seven barley chromosomes. Average LD was observed to decay below a critical level (r2-value 0.2) within a map distance of 5-10 cM. Phenotypic variation within the panel was reasonably large for all the traits. The heritabilities calculated for each trait over multi-environment experiments ranged between 0.90-0.95. Different statistical models were tested to control spurious LD caused by population structure and to calculate the P-value of marker-trait associations. Using a mixed linear model with kinship for controlling spurious LD effects, we found a total of 171 significant marker trait associations, which delineate into 107 QTL regions. Across all traits these can be grouped into 57 novel QTL and 50 QTL that are congruent with previously mapped QTL positions. CONCLUSIONS: Our results demonstrate that the described diverse barley panel can be efficiently used for GWAS of various quantitative traits, provided that population structure is appropriately taken into account. The observed significant marker trait associations provide a refined insight into the genetic architecture of important agronomic traits in barley. However, individual QTL account only for a small portion of phenotypic variation, which may be due to insufficient marker coverage and/or the elimination of rare alleles prior to analysis. The fact that the combined SNP effects fall short of explaining the complete phenotypic variance may support the hypothesis that the expression of a quantitative trait is caused by a large number of very small effects that escape detection. Notwithstanding these limitations, the integration of GWAS with biparental linkage mapping and an ever increasing body of genomic sequence information will facilitate the systematic isolation of agronomically important genes and subsequent analysis of their allelic diversity.